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Description

What is
MongoDB?

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

What is
Datomic?

Build flexible, distributed systems that can leverage the entire history of your critical data, not just the most current state. Build them on your existing infrastructure or jump straight to the cloud.

What is
IronDB?

IronDB is the best way to store persistent key-value data in the browser. Data saved to IronDB is redundantly stored in Cookies, IndexedDB, LocalStorage, and SessionStorage, and relentlessly self heals if any data therein is deleted or corrupted.

How developers use MongoDB vs Datomic vs IronDB

MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.

Used to be MySQL, but once moved to MongoDB, everything just speed up dramatically, data became pretty and easy to work with. Sophisticated aggregations allow us to run complicated analytics anytime as easy as possible.

Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.

Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013.
It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations.
Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.

MongoDB is our prefered document store for pretty much all non-relational-heavy development. MongoDB scales beautifully and provides us with very polished api's and drivers. Easy to use, flexible and scalable.

While the huge majority of BI data comes from 3rd-party sources, some pieces require ad-hoc sources - this is largely where Mongo comes into play. Views such as "Activity Log" need on-the-fly recordkeeping that's best manually entered; considering that fetching from a task manager API will paint an overwhelming or inaccurate picture of the month's activity.

We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by agenda. If it works out well we might look to see where it could become a primary document storage engine for us.